Proving Properties of Countable Sets & Probability Spaces

In summary, for problem 1, you can prove the forward implication by assuming without loss of generality that X is a subset of the real numbers, and for the reverse implication, show that the cardinality of the Σ-algebra generated by any countable family of sets is at most the continuum. For problem 2, you can use a counting method by defining S_n as the set of points that appear in exactly n of the A_i and considering the total measure of these sets with multiplicity. This can help you prove that there exists a point that appears in at least 1005 of the A_i. Additionally, for problem 1, the set of (p,q) for p,q in Q intersected with X
  • #1
hellbike
61
0
1.prove that for any set X: |X|<c <=> in P(X) exist such countable set family F, that sigma algebra generated by F contains all points.

2.let (X,E,u) be probability space and A_1,...,A_2009 in E have property u(A_i)>=1/2. Prove that there exist x such is in A_i for atleast 1005 different i.

i need some tip in solving this.
 
Last edited:
Physics news on Phys.org
  • #2
On problem 1, for the forward implication, note that if |X| ≤ c, then you can assume WLOG that X⊆R. For the reverse, show that the cardinality of the Σ-algebra generated by any countable family of sets is at most the continuum.

For problem 2, try applying a counting method. Specifically, define S_n as the set of all points that are in exactly n of the A_i. What is ∑nμ(S_n)?
 
  • #3
What do you mean by counting method? Induction?

And this sum is between 1/2 and 1, but i don't know why this would be useful?

and for problem 1:
->
is this sufficient to say that set of (p,q) for p,q in Q can do the job for any subset of R?
 
Last edited:
  • #4
hellbike said:
What do you mean by counting method? Induction?

I mean extending combinatorial methods to the measure-theoretic setting. In this case, we want to "count" (i.e. find the measure of) the set of points in the A_i with multiplicity (i.e. a point that appears in two different A_i would be counted twice) in two different ways. First, we divide it up into the disjoint sets S_n, and observe that each point in S_n will be counted exactly n times, so the total measure (with multiplicity) is [itex]\sum_{n=1}^{2009}n\mu (S_n)[/itex]. On the other hand, each point is counted once each time it appears in an A_i, so the total measure (with multiplicity) is [itex]\sum_{i=1}^{2009}\mu (A_i)[/itex]. So these two sums are equal. Can you see how this implies that μ(S_n)>0 for at least one n≥1005?

is this sufficient to say that set of (p,q) for p,q in Q can do the job for any subset of R?

Nearly, although since you're generating a Σ-algebra on X, you should write [itex](p,\ q) \cap X,\ p,\ q\in\mathbb{Q}[/itex]
 
  • #5
yes, thank you very much.
 

FAQ: Proving Properties of Countable Sets & Probability Spaces

What is the definition of a countable set?

A countable set is a set that has a finite number of elements or can be put into a one-to-one correspondence with the set of natural numbers.

What is the difference between a countable set and an uncountable set?

The main difference is that a countable set has a finite or countably infinite number of elements, while an uncountable set has an uncountably infinite number of elements. This means that you can list all the elements in a countable set, but you cannot list all the elements in an uncountable set.

How can we prove the properties of countable sets?

There are several methods for proving properties of countable sets, including using mathematical induction, set theory principles, and bijections. These methods allow us to prove properties such as the cardinality of a countable set, the existence of countably infinite subsets, and the existence of countably infinite unions and intersections.

What is a probability space?

A probability space is a mathematical construct that consists of a set of possible outcomes, a set of events, and a probability measure that assigns a probability to each event. It is used to model and analyze random phenomena and is an essential concept in probability theory and statistics.

How do we use probability spaces to calculate probabilities?

To calculate probabilities in a probability space, we use the axioms and properties of probability, such as the addition rule and the multiplication rule. We also use combinatorial techniques, such as permutations and combinations, to determine the number of possible outcomes and events in a given probability space. Additionally, we can use probability distributions and statistical tools to analyze and make predictions about the outcomes of random events in a probability space.

Back
Top